发布时间 : 星期六 文章2010年上海世博会经济影响力的定量评估资料更新完毕开始阅读
附录
程序1：
b=[1,2,4,1/2;1/2,1,2,1/3;1/4,1/2,1,1/5;2,3,5,1] b =1.0000 2.0000 4.0000 0.5000 0.5000 1.0000 2.0000 0.3333 0.2500 0.5000 1.0000 0.2000 2.0000 3.0000 5.0000 1.0000 >> [x,lumda]=eig(b)
x = 0.4932 0.1700 - 0.4437i 0.1700 + 0.4437i 0.5345 0.2635 0.0940 + 0.0255i 0.0940 - 0.0255i -0.8018 0.1385 0.0506 + 0.1117i 0.0506 - 0.1117i 0.2673 0.8174 -0.8659 -0.8659 -0.0000 lumda = 4.0211 0 0 0 -0.0106 + 0.2913i 0 0 0 0 -0.0106 - 0.2913i 0 0 0 0 0.0000 >> r=abs(sum(lumda))
r = 4.0211 0.2915 0.2915 0.0000 >> n=find(r==max(r)) n =1
>> max_lumda_b=x(:,n) max_lumda_b =0.4932 0.2635 0.1385 0.8174
>> w=x(:,n)/sum((x(:,n))) w = 0.2880 0.1539 0.0809 0.4773
>> max_lumda_b=lumda(n,n) max_lumda_b = 4.0211
程序2：
a=[1 1/2 1/2;2 1 3/2;2 2/3 1]
a = 1.0000 0.5000 0.5000 2.0000 1.0000 1.5000 2.0000 0.6667 1.0000 >> [x,lumda]=eig(a)
x = 0.3280 0.1640 + 0.2841i 0.1640 - 0.2841i 0.7510 -0.7510 -0.7510 0.5731 0.2865 - 0.4963i 0.2865 + 0.4963i
lumda =3.0183 0 0 0 -0.0091 + 0.2348i 0 0 0 -0.0091 - 0.2348i >> r=abs(sum(lumda))
r =3.0183 0.2350 0.2350 >> n=find(r==max(r)) n =1
>> max_lumda_a=lumda(n,n) max_lumda_a =
3.0183
w=x(:,n)/sum(x(:,n)) w = 0.1985 0.4546 0.3469
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程序3：
d1=[0.29,0.15,0.08,0.48]
d1 =0.2900 0.1500 0.0800 0.4800 >> d2=[0.20,0.45,0.35]
d2 = 0.2000 0.4500 0.3500
>> a4=[0.5,0.3,0.1,0.05,0.05;0.4,0.2,0.2,0.1,0.1;0.2,0.3,0.4,0.05,0.05;0.3,0.3,0.2,0.1,0.1] a4 = 0.5000 0.3000 0.1000 0.0500 0.0500 0.4000 0.2000 0.2000 0.1000 0.1000 0.2000 0.3000 0.4000 0.0500 0.0500 0.3000 0.3000 0.2000 0.1000 0.1000 >> b4=[0.4,0.3,0.1,0.1,0.1;0.3,0.3,0.1,0.2,0.1;0.2,0.3,0.1,0.3,0.1]
b4 = 0.4000 0.3000 0.1000 0.1000 0.1000 0.3000 0.3000 0.1000 0.2000 0.1000 0.2000 0.3000 0.1000 0.3000 0.1000 >> p=[0.4,0.6]
p = 0.4000 0.6000 >> m1=d1*a4
m1 = 0.3650 0.2850 0.1870 0.0815 0.0815 >> m2=d2*b4
m2 = 0.2850 0.3000 0.1000 0.2150 0.1000 >> R=p*[m1;m2]
R =0.3170 0.2940 0.1348 0.1616 0.0926 >> z1=m1*[95;85;75;65;55] z1 = 82.7050
>> z2=m2*[95;85;75;65;55] z2 =79.5500
>> z3=R*[95;85;75;65;55] z3 = 80.8120 程序4：
t=[3 4 5 6 7 8]
s=[8.8090 8.9963 9.1230 9.2463 9.4083 9.5250] T=[ones(6,1) t' (t.^2)'];
[b,bint,r,rint,stats]=regress(s',T); b,stats
t = 3 4 5 6 7 8
s = 8.8090 8.9963 9.1230 9.2463 9.4083 9.5250 b =
8.3051 0.1827 -0.0038
stats = 0.9972 529.1794 0.0002 0.0003
plot(t,s,'r') >> hold on
>> fplot('8.3051+0.1827*t-0.0038*t^2',[3 8]) >> hold on
title('上海GDP对数值与年份关系');
xlabel('年份-2000'),ylabel('上海GDP对数值') legend('实际数据','拟合曲线')
程序5：
t=[2452.11 3084.66 3542.55 3925.09 4458.61 4829.45]/1000
s=[8.8090 8.9963 9.1230 9.2463 9.4083 9.5250] plot(t,s,'r')
t = 2.4521 3.0847 3.5426 3.9251 4.4586 4.8294
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s = 8.8090 8.9963 9.1230 9.2463 9.4083 9.5250 plot(t,s,'r') hold on
>> fplot('8.0662+ 0.3010*t',[2 5]) hold on
title('上海GDP对数值与投资总额关系');
xlabel('投资总额/1000'),ylabel('上海GDP对数值') legend('实际数据','拟合曲线')
程序6：
Q1参数确定：
x1=[1997-2000 1998-2000 1999-2000 2000-2000 2001-2000]
x2=[1977.59/1000 1964.83/1000 1856.72/1000 1869.67/1000 1994.73/1000] y=[8.1429 8.2430 8.3402 8.4703 8.5584]';
X=[ones(5,1) x1' x2' (x1.^2)']; [b,bint,r,rint,stats]=regress(y,X); b,stats x1 =
-3 -2 -1 0 1 x2 =
1.9776 1.9648 1.8567 1.8697 1.9947 b =
8.6278 0.1117 -0.0904 0.0032
stats =0.9974 130.2157 0.0643 0.0003 >> y=8.6278+0.0032*4+0.1117*2-0.0904*2.18706 y = 8.6663 >> exp(y)
ans = 5.8039e+003 >> (y-5741.03)/5741.03 ans =-0.9985
>> (exp(y)-5741.03)/5741.03 ans =
0.0110 程序7：
Q2参数确定：
x1=[2003-2000 2004-2000 2005-2000 2006-2000 2007-2000]
x2=[2452.11/1000 3084.66/1000 3542.55/1000 3925.09/1000 4458.61/1000] y=log([6694.23 8072.83 9164.10 10366.37 12188.85]'); X=[ones(5,1) x1' x2' (x1.^2)']; [b,bint,r,rint,stats]=regress(y,X); b,stats
x1 = 3 4 5 6 7
x2 =2.4521 3.0847 3.5426 3.9251 4.4586 b =
8.1211 -0.0176 0.2955 0.0019
stats = 1.0e+003 *
0.0010 1.7622 0.0000 0.0000
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